// Copyright (c) 2025 Huawei Technologies Co., Ltd
// All rights reserved.
//
// Licensed under the BSD 3-Clause License  (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include "op_plugin/AclOpsInterface.h"
#include "op_plugin/OpApiInterface.h"
#include "op_plugin/utils/op_api_common.h"

namespace op_api {
    using npu_preparation = at_npu::native::OpPreparation;
    using npu_utils = at_npu::native::NpuUtils;
    using tensor_list = std::tuple<at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor>;
    const int DIM_TWO = 2;

tensor_list npu_moe_distribute_dispatch(const at::Tensor &x, const at::Tensor &expert_ids,
                                        c10::string_view group_ep, int64_t ep_world_size, int64_t ep_rank_id,
                                        int64_t moe_expert_num,
                                        const c10::optional<at::Tensor> &scales,
                                        c10::string_view group_tp, int64_t tp_world_size, int64_t tp_rank_id,
                                        int64_t expert_shard_type, int64_t shared_expert_rank_num,
                                        int64_t quant_mode, int64_t global_bs)
{
    TORCH_CHECK((x.dim() == 2) && (expert_ids.dim() == 2), "The x and expert_ids should be 2D", OPS_ERROR(ErrCode::PARAM));
    TORCH_CHECK((x.scalar_type() == at::kBFloat16 || (x.scalar_type() == at::kHalf)) && (expert_ids.scalar_type() == at::kInt),
                "dtype of x should be bfloat16 or half, dtype of expert_ids should be int.", OPS_ERROR(ErrCode::PARAM));
    TORCH_CHECK((shared_expert_rank_num > 0) && (shared_expert_rank_num < ep_world_size),
                "shared_expert_rank_num should be in (0, ep_world_size)", OPS_ERROR(ErrCode::PARAM));
    auto x_size = x.sizes();
    auto expert_ids_size = expert_ids.sizes();

    int n = x_size[0];
    int h = x_size[1];
    int k = expert_ids_size[1];

    bool shared_front = (expert_shard_type == 0) ? true : false;
    int local_moe_expert_num = 0;
    int global_bs_real = (global_bs == 0) ? (n * ep_world_size) : global_bs;
    int a = 0;
    if (shared_front) {
        if (ep_rank_id < shared_expert_rank_num) {
            local_moe_expert_num =  1;
            a = global_bs_real / shared_expert_rank_num;
        } else {
            local_moe_expert_num = moe_expert_num / (ep_world_size - shared_expert_rank_num);
            a = global_bs_real * local_moe_expert_num;
        }
    } else {
        if (ep_rank_id >= ep_world_size - shared_expert_rank_num) {
            local_moe_expert_num = 1;
            a = global_bs_real / shared_expert_rank_num;
        } else {
            local_moe_expert_num = moe_expert_num / (ep_world_size - shared_expert_rank_num);
            a = global_bs_real * local_moe_expert_num;
        }
    }

    auto output_dtype = (!scales.has_value() && quant_mode == 0) ? x.scalar_type() : at::kChar;
    char *group_ep_ptr = const_cast<char *>(group_ep.data());
    std::string group_tp_str = std::string(group_tp);
    char *group_tp_ptr = const_cast<char *>(group_tp_str.c_str());
    at::Tensor expand_x = npu_preparation::apply_tensor_without_format({a * tp_world_size, h}, x.options().dtype(output_dtype));
    at::Tensor dynamic_scales = npu_preparation::apply_tensor_without_format({a * tp_world_size}, x.options().dtype(at::kFloat));
    at::Tensor expand_idx = npu_preparation::apply_tensor_without_format({n * k}, x.options().dtype(at::kInt));
    TORCH_CHECK(local_moe_expert_num == 1, "local_moe_expert_num should be 1:", local_moe_expert_num);
    at::Tensor expert_token_nums = npu_preparation::apply_tensor_without_format({local_moe_expert_num}, x.options().dtype(at::kLong));
    at::Tensor ep_recv_counts = npu_preparation::apply_tensor_without_format({ep_world_size}, x.options().dtype(at::kInt));
    at::Tensor tp_recv_counts = npu_preparation::apply_tensor_without_format({tp_world_size}, x.options().dtype(at::kInt));
    EXEC_NPU_CMD(aclnnMoeDistributeDispatch, x, expert_ids, scales,
                 group_ep_ptr, ep_world_size, ep_rank_id,
                 moe_expert_num,
                 group_tp_ptr, tp_world_size, tp_rank_id,
                 expert_shard_type, shared_expert_rank_num,
                 quant_mode, global_bs_real, expand_x, dynamic_scales, expand_idx,
                 expert_token_nums, ep_recv_counts, tp_recv_counts);
    return std::tie(expand_x, dynamic_scales, expand_idx, expert_token_nums, ep_recv_counts, tp_recv_counts);
}
}
